Development and validation of detailed building, plant and controller modelling to demonstrate interactive behaviour of system components

As plant modelling becomes capable of more complexity and detailed resolution, new opportunities arise for the virtual evaluation of discrete plant components such as flow control and energy conversion devices, and controllers. Such objects are conventionally developed and tested at the prototype stage in a laboratory environment. Designers now seek to use modelling technology to extend their understanding from limited laboratory test results to full building and plant system analysis. This paper describes the development of a modelling system, using ESP-r, for typical United Kingdom domestic house types with hydronic gas or oil fired central heating including radiator and underfloor heating systems, and with a variety of conventional or advanced control types. It demonstrates the ability of detailed building and plant modelling to reveal unexpected insights into how real control systems perform in combination with other plant items and in different building types, including estimation of their influence on annual energy consumption. Comparisons with measurements taken in test rooms confirm that the observed behaviour of controls is realised in practice. The authors conclude that the complex dynamic interactions that take place between the various elements that make up a real building energy system have an important influence on its overall energy performance, revealing causes of variance that cannot be identified by laboratory testing alone, or by simplistic energy assessment tools

An Assessment of Alternative State Space Models for Count Time Series

This paper compares two alternative models for autocorrelated count time series. The first model can be viewed as a 'single source of error' discrete state space model, in which a time-varying parameter is specified as a function of lagged counts, with no additional source of error introduced. The second model is the more conventional 'dual source of error' discrete state space model, in which the time-varying parameter is driven by a random autocorrelated process. Using the nomenclature of the literature, the two representations can be viewed as observation-driven and parameter-driven respectively, with the distinction between the two models mimicking that between analogous models for other non-Gaussian data such as financial returns and trade durations. The paper demonstrates that when adopting a conditional Poisson specification, the two models have vastly different dispersion/correlation properties, with the dual source model having properties that are a much closer match to the empirical properties of observed count series than are those of the single source model. Simulation experiments are used to measure the finite sample performance of maximum likelihood (ML) estimators of the parameters of each model, and ML-based predictors, with ML estimation implemented for the dual source model via a deterministic hidden Markov chain approach. Most notably, the numerical results indicate that despite the very different properties of the two models, predictive accuracy is reasonably robust to misspecification of the state space form.Discrete state-space model; single source of error model; hidden Markov

Nuclear magnetic resonance spectroscopy: Abnormal splitting of ethyl groups due to molecular asymmetry

Nuclear magnetic resonance (n.m.r.) spectroscopy provides an excellent means for qualitative identification of ethyl groups by use of the familiar three-four pattern of spin-spin splitting (1). It has been observed previously (2) that the methylene protons of systems of the type R-CH2-CR1R2R3 (where R1 can be the same as R or different) may be magnetically nonequivalent and display AB rather than A2-type spectra (3). We now wish to report several examples of this type of behavior with ethyl groups, particularly ethoxy groups, knowledge of which could be important to anyone using n.m.r. for organic qualitative analysis

Computer and Internet Use by Great Plains Farmers

This study uses data from a 2001 survey of Great Plains farmers to explore the adoption, usage patterns, and perceived benefits of computers and the Internet. Adoption results suggest that exposure to the technology through college, outside employment, friends, and family is ultimately more influential than farmer age and farm size. Notably, about half of those who use the Internet for farm-related business report zero economic benefits from it. Whether a farmer perceives that the Internet generates economic benefits depends primarily on how long the farmer has used the Internet for farm business and for what purposes.agriculture, competitiveness, net benefits, technology adoption, Research and Development/Tech Change/Emerging Technologies,

Computer and Internet Use by Great Plains Farmers

We use data from a 2001 survey of Great Plains farmers to explore the adoption, usage patterns, and perceived benefits of computers and the Internet. Our adoption results suggest that exposure to the technology through college, outside employment, friends, and family is ultimately more influential than farmer age and farm size. Notably, about half of those who use the Internet for farm-related business report zero economic benefits from it. Whether a farmer perceives that the Internet generates economic benefits depends primarily on how long the farmer has used the Internet for farm business and for what purposes.Farm Management, Research and Development/Tech Change/Emerging Technologies,

Research Mentoring and Scientist Identity: Insights from Undergraduates and their Mentors

Background Mentored research apprenticeships are a common feature of academic outreach programs that aim to promote diversity in science fields. The current study tests for links between three forms of mentoring (instrumental, socioemotional, and negative) and the degree to which undergraduates psychologically identify with science. Participants were 66 undergraduate-mentor dyads who worked together in a research apprenticeship. The undergraduate sample was predominantly composed of women, first-generation college students, and members of ethnic groups that are historically underrepresented in science. Results Findings illustrated that undergraduates who reported receiving more instrumental and socioemotional mentoring were higher in scientist identity. Further, mentors who reported engaging in higher levels of negative mentoring had undergraduates with lower scientist identity. Qualitative data from undergraduates’ mentors provided deeper insight into their motivation to become mentors and how they reason about conflict in their mentoring relationships. Conclusions Discussion highlights theoretical implications and details several methodological recommendations